Remove Auto-classification Remove Auto-complete Remove BERT Remove Explainability
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Interfaces for Explaining Transformer Language Models

Jay Alammar

This article focuses on auto-regressive models, but these methods are applicable to other architectures and tasks as well. input saliency is a method that explains individual predictions. The literature is most often concerned with this application for classification tasks, rather than natural language generation.

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Simplify Deployment and Monitoring of Foundation Models with DataRobot MLOps

DataRobot Blog

Then you can use the model to perform tasks such as text generation, classification, and translation. As an example, getting started with a BERT model for question answering (bert-large-uncased-whole-word-masking-finetuned-squad) is as easy as executing these lines: !pip pip install transformers==4.25.1 datarobot==3.0.2

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Dialogue-guided visual language processing with Amazon SageMaker JumpStart

AWS Machine Learning Blog

The system is further refined with DistilBERT , optimizing our dialogue-guided multi-class classification process. Additionally, you benefit from advanced features like auto scaling of inference endpoints, enhanced security, and built-in model monitoring. Please explain the main clinical purpose of such image?Can

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Creating An Information Edge With Conversational Access To Data

Topbots

On a more advanced stance, everyone who has done SQL query optimisation will know that many roads lead to the same result, and semantically equivalent queries might have completely different syntax. 3] provides a more complete survey of Text2SQL data augmentation techniques. different variants of semantic parsing.